Subspace-based MRS data quantitation of multiplets using prior knowledge
Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated
signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several
subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms.
However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy
and resolution.